Abstract
The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO‐SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local‐search ability. The proposed HPSO‐SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO‐SA algorithms. In this paper, we provide also two versions of HPSO‐SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO‐SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO‐SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.
Highlights
Several optimization algorithms have been developed over last few decades for solving real-world optimization problems
In order to make a fair comparison between classical Particle Swarm Optimization (PSO), Attraction-Repulsion based PSO (ATREPSO), QIPSO, Gaussian Mutation based PSO (GMPSO) and HPSO-Simulated Annealing (SA) approach, we fixed, as indicated in [2], the same seed for random number generation so that the initial swarm population is same for all five algorithms
In order to compute the energy cost of studied memory architecture composed by an Scratch-Pad Memories (SPMs), an instruction cache and a DRAM, we proposed an energy consumption estimation model which is explained in [11]
Summary
Several optimization algorithms have been developed over last few decades for solving real-world optimization problems. We have many heuristics like Simulated Annealing (SA) [1] and optimization algorithms that make use of social or evolutionary behaviors like Particle Swarm Optimization (PSO) [2, 3]. Particle Swarm Optimization (PSO) is based on the social behavior of individuals living together in groups. Each individual tries to improve itself by observing other group members and imitating the better ones This way, the group members are performing an optimization procedure which is described in [3]. We present a hybrid optimization algorithm, called HPSO-SA, which exploits intuitively the positive features of PSO and SA.
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